Random body movement cancellation for non-contact vital sign detection

ABSTRACT

A method and system for cancelling body movement effect for non-contact vital sign detection is described. The method begins with sending on a first electromagnetic wave transceiver a first electromagnetic signal with a first frequency to a first side of a body, such as a person or animal. Simultaneously using a second electromagnetic wave transceiver a second electromagnetic signal is sent with a second frequency to a second side of a body, wherein the first frequency and the second frequency are different frequencies. A first reflected electromagnetic signal reflected back in response to the first electromagnetic wave on the first transceiver is received and a first baseband complex signal is extracted. Likewise a second reflected electromagnetic signal reflected back in response to the second electromagnetic wave on the second transceiver is received and a second baseband complex signal is extracted. The first baseband complex signal is mathematically combined with the second baseband complex signal to cancel out a Doppler frequency drift therebetween to yield a periodic Doppler phase effect.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is the U.S. National Stage Application ofInternational Patent Application No. PCT/US2008/069766, filed Jul. 11,2008, which claims the benefit of U.S. Provisional Patent ApplicationSer. No. 60/949,285, filed on Jul. 12, 2007, both of which are hereinincorporated by reference herein in their entirety, including anyfigures, tables, or drawings.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not Applicable.

FIELD OF THE INVENTION

The present invention relates generally to non-contact monitoring andmore specifically a method and system to eliminate random body movementsduring non-contact vital sign monitoring.

BACKGROUND OF THE INVENTION

In practical applications of non-contact vital sign detection, the noisecaused by irregular body movement presents severe interference foraccurate detection of vital signs such as respiration and heartbeatsignal. Since random body movement is comparable or even stronger thanthe weak vital sign signal, to some extent it is the main factorlimiting the applications of non-contact vital sign sensors.

To reduce body movement, prior art techniques require that the subjector patient being monitored remain stationary and motionless. In manyapplications such as healthcare, sports, law enforcement, security, itis difficult if not impossible to have the subject being monitored toremain stationary.

Accordingly, what is needed is a method and a system to overcome theaforementioned problems and to recover severely distorted signals duringnon-contact vital sign detection even when the subject is notstationary.

SUMMARY OF THE INVENTION

The present invention provides a method and a system to cancel out noisedue to random body movement during non-contact vital sign monitoring.The present invention recovers severely distorted signal to obtainaccurate measurement result, solving the main problem prohibiting thewide daily application of non-contact vital sensors.

Described is a random body movement cancellation in quadrature Dopplerradar non-contact vital sign detection using complex signal demodulationand the arctangent demodulation. Applications using the presentinvention include sleep apnea monitor, lie detector, and baby monitor toeliminate the false alarm caused by random body movement. It has beenshown that if the DC offset of the baseband signal is accuratelycalibrated, both demodulation techniques can be used for random bodymovement cancellation. While the complex signal demodulation is lesslikely to be affected by a DC offset, the arctangent demodulation hasthe advantage of eliminating harmonic and intermodulation interferenceat high carrier frequencies. In applications where the DC offset cannotbe accurately calibrated, the complex signal demodulation is used.Ray-tracing model is used to show the effects of constellationdeformation and optimum/null detection ambiguity caused by the phaseoffset due to finite antenna directivity. Experiments have beenperformed using 4-7 GHz radar

In one embodiment the present invention method for cancelling randombody by sending at least two electromagnetic signals comprising a firstelectromagnetic signal with a first frequency to a first side of a bodyfrom a first electromagnetic wave transceiver and a secondelectromagnetic signal with a second frequency to a second side of abody from a second electromagnetic wave transceiver. A first reflectedelectromagnetic signal reflected back in response to the firstelectromagnetic wave on the first transceiver is received and a firstbaseband complex signal is extracted. Likewise a second reflectedelectromagnetic signal reflected back in response to the secondelectromagnetic wave on the second transceiver is received and a secondbaseband complex signal is extracted. The first baseband complex signalis mathematically combined with the second baseband complex signal tocancel out a Doppler frequency drift therebetween to yield a periodicDoppler phase effect. Vital signs such as respiration rate and heartrate are extracted from the signal representing the periodic Dopplerphase effect.

The foregoing and other features and advantages of the present inventionwill be apparent from the following more particular description of thepreferred embodiments of the invention, as illustrated in theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject matter, which is regarded as the invention, is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The foregoing and other features, and advantages ofthe invention will be apparent from the following detailed descriptiontaken in conjunction with the accompanying drawings in which:

FIG. 1 is a system diagram of the multi-antenna and multi-wavelengthtechnique, according to the present invention.

FIG. 2 is a block diagram illustrating: (a) a complex signaldemodulation; and (b) an arctangent demodulation, according to thepresent invention.

FIG. 3 is a graph illustrating: (a) ray-tracing model and the angularinformation (b) ray-tracing model of signals reflected from point A andB on the body using a 5.8 GHz radar, according to the present invention.

FIG. 4. is a ray-tracing model illustrating: (a) the phase offset on thesurface of human body radiated by a 5.8 GHz radar; (b) a 7 by 7 elementsantenna array's radiation intensity on the human body; (c) approximationof the normalized amplitude of body movement caused by respiration; and(d) approximation of the normalized amplitude of body movement caused byheartbeat, according to the present invention.

FIG. 5 is a graph of demodulation for a 5.8 GHz radar illustrating: (a)a signal detected at heart center (Case I) and at body center (Case II);(b) an actual received signal (Case III); (c) an angular informationψ(t) of the received signal; and (d) baseband spectra obtained by thecomplex signal demodulation and the arctangent demodulation (the DCcomponent is not shown in the baseband spectrum).

FIG. 6 is a graph of demodulation for a 24 GHz radar illustrating: (a) asignal detected at heart center (Case I) and at body center (Case II);(b) an actual received signal (Case III), with the recovered angularinformation shown in inset; (c) a baseband spectra obtained by thecomplex signal demodulation and the arctangent demodulation (DCcomponent not shown in the spectra), according to the present invention.

FIG. 7 is a graph of a baseband spectrum detected by the I and the Qchannels with a carrier frequency of 24 GHz illustrating: (a) a spectrumof a single-beam signal projected to the heart center; and (b) aspectrum of the actually received signal, according to the presentinvention.

FIG. 8. is a graph of baseband spectra obtained when random bodymovement is present illustrating: (a) the random body movement is shownin the Z, X, and Y directions, which are defined in FIG. 3; and (b) abaseband spectra by arctangent demodulation (AD) and complex signaldemodulation (CSD), according to the present invention.

FIG. 9. is a graph of illustrating: (a) angular information and basebandspectrum; and (b) angular information recovered by random body movementcancellation (RBMC) using the two demodulation techniques; accurate DCinformation is used in demodulation but not shown in the spectrum,according to the present invention.

FIG. 10. is a graph illustrating: (a) angular information and basebandspectrum; and angular information recovered from the random bodymovement cancellation (RBMC) technique; the random body movements aremodeled in three dimensions, and the DC offset in each transceiver is30% of the maximum signal amplitude, according to the present invention.

FIG. 11 is a block diagram of the 4-7 GHz radar transceiver as shown inFIG. 1, according to the present invention.

FIG. 12 is a graph of DC offset estimation illustrating: (a) atrajectory of detected baseband signal with no DC information and withestimated DC offset level added; and (b) a spectra obtained by the twodemodulation techniques. Signal with estimated DC offset added was usedfor arctangent demodulation.

FIG. 13 is a graph of signals detected from: (a) the front of a humanbody; and (b) the back of the human body, according to the presentinvention.

FIG. 14 is a graph of random body movement cancellation using arctangentdemodulation illustrating: (a) a spectra measured from the front and theback of the human body; (b) a spectrum from combining the twotransceiver outputs, the heartbeat information cannot be recovered dueto inaccurate DC offset information, according to the present invention.

FIG. 15 is a graph of random body movement cancellation using complexsignal demodulation illustrating: (a) a spectra measured from the frontand the back of the human body; and (b) an output spectrum by the randombody movement cancellation technique, the heartbeat information isrecovered, according to the present invention.

FIG. 16 is a flow diagram of the overall random body movementcancellation, according to the present invention.

FIG. 17 is a generalized block diagram of a computer system useful forimplementing the noise cancellation algorithm according to the presentinvention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

It should be understood that these embodiments are only examples of themany advantageous uses of the innovative teachings herein. In general,statements made in the specification of the present application do notnecessarily limit any of the various claimed inventions. Moreover, somestatements may apply to some inventive features but not to others. Ingeneral, unless otherwise indicated, singular elements may be in theplural and vice versa with no loss of generality. The term “body” or“patient” or “person” or “subject” is used interchangeably herein torefer to any living organism that has vital signs such as heart beat andrespiration including humans and animals.

I. INTRODUCTION

The present invention has many advantages over the prior art system. Oneadvantage is the present invention cancels out noise due to random bodymovement during non-contact vital sign monitoring. The present inventionrecovers severely distorted signal to obtain accurate measurementresult, solving the main problem prohibiting the wide daily applicationof non-contact vital sensors.

Further, the present invention does not require subjects to remainstationary and motionless. It makes the existing applications ofnon-contact sensing more robust. This has broad application in suchdiverse areas as healthcare, sports, law enforcement, security, socialnetworking where the subjects may move around during detection. Improvedapplications include monitoring systems, biomedical sensors, liedetectors, military personal radar carried by soldiers forbehind-the-wall sensing, and security systems. All of the above systemsand applications are non-contact and can be made portable.

In one embodiment the present invention uses a complex signaldemodulation used for random body movement cancellation. In anotherembodiment of the present invention uses an important demodulationmethod of non-contact vital sign detection, i.e. the arctangentdemodulation, for random body movement cancellation. It is shown that ifthe baseband DC offset information is known, both of the twodemodulation techniques can be used for random body movementcancellation. When the DC offset cannot be accurately calibrated out,the complex signal demodulation is more favorable for random bodymovement cancellation. The ray-tracing model used illustrates theeffects of constellation deformation and optimum/null detectionambiguity caused by the phase offset due to finite antenna directivity.

Two modulations embodiments are described in order to mathematicallycombine the first baseband complex signal with the second basebandcomplex signal to cancel out a Doppler frequency drift in order to yielda periodic Doppler phase effect. The two demodulation embodiments andtheir implementation for random body movement cancellation are describedbelow in the section entitled II Complex-Signal Demodulation andArctangent Demodulation. The effect of phase offset is discussed inSection III. Simulations have been performed and the results arereported in Section IV. Experimental results are presented in Section V,and a conclusion is drawn in Section VI.

II. COMPLEX-SIGNAL DEMODULATION AND ARCTANGENT DEMODULATION

The present invention utilizes a multi-antenna and multi-wavelengthtechnique that combines signals detected from different body orientationto cancel out random body movements based on different Doppler frequencyshifts detected. This technique can recover severely distorted signal toachieve robust non-contact measurement of heartbeat rate and respirationrate from a distance away. This invention includes the theory/method andboth the hardware system and software algorithm to implement the method.When random body movement is present and it affects accurate detection,the measurement has to be performed simultaneously from both sides tocancel out the random frequency drift. This is described further in thepublication by C. Li, and J. Lin, “Complex Signal Demodulation andRandom Body Movement Cancellation Techniques for Non-contact Vital SignDetection,” IEEE MTT-S International Microwave Symposium Digest, June,2008, which is hereby incorporate by reference in its entirety.

Turning now to FIG. 1, shown is a system diagram 100 or Doppler radarnon-contact vital sign detection. The system 100 includes themulti-transceiver 102, 112 and multi-wavelength technique 104 and 114i.e. λ₁ and λ₂. The two transceivers, one in front of and the otherbehind the subject 160, are transmitting and receiving signals withdifferent wavelength to avoid interference to each other. It isimportant to note that although the transceivers are shown in front andback, other positions such as one side of the body and the other side ofthe body can be used.

In one embodiment, each transceiver 102 and 112 is identical. A signalgenerator 122 is fed into a splitter 124. One output of the splitter isfed to a quadrature splitter 126 and the other output fed to thetransmitter output through a circulator 120 producing wave λ₂ 114. Thesignal received which is reflected off the subject 160 throughcirculator 120 is fed to multipliers 128 and 130 followed by aquadrature splitter 126 to produce an output. The output quadrature Qand in-phase I component of each down sampled signal from eachtransceiver 102 and 112 are directed to a respective DAQ (digitalacquisition module) 132, 142. Each DAQ 132 and 142 is fed to a movementcancellation algorithm 150. In one embodiment the body movementcancellation algorithm 150 is implemented as part of a computer. Morespecifically the body movement cancellation algorithm 150 is implementedin software to process the signals detected from different transceivers.The resulting detected wave is outputted to a display 152 or otheroutput device such as a printer, buzzer, or wireless to a remotemonitoring station (not shown). This algorithm is described further inthe sections below. It is important to note that the present inventioncan be implemented in a combination of hardware and software, such asdedicated hardware system and that the present invention is not limitedto using a computer to implement this algorithm.

In the analysis of non-contact quadrature demodulation of vital sign,the single-beam model assumes an ideal antenna with infinite directivityfocusing a beam at the location of the heart. When no random bodymovement is present, the normalized detected baseband signal in one ofthe baseband I/Q channels can be represented and analyzed by spectralanalysis:

$\begin{matrix}\begin{matrix}{{B(t)} = {\cos\left( {\frac{4\pi\;{x_{h}(t)}}{\lambda} + \frac{4\pi\;{x_{r}(t)}}{\lambda} + \phi} \right)}} \\{= {\sum\limits_{k = {- \infty}}^{\infty}{\sum\limits_{l = {- \infty}}^{\infty}{{J_{l}\left( \frac{4\pi\; m_{h}}{\lambda} \right)}{J_{k}\left( \frac{4\pi\; m_{r}}{\lambda} \right)}{\cos\left( {{k\;\omega_{r}t} + {l\;\omega_{h}t} + \phi} \right)}}}}}\end{matrix} & (1)\end{matrix}$

where xh(t)=mh·sin ωht, xr(t)=mr·sin ωrt are the periodic body movementsdue to heartbeat and respiration, λ is the wavelength of the wirelesssignal, φ is the total residual phase accumulated in the circuit andalong the transmission path, and Jn is the Bessel function of the firstkind.

For a quadrature transceiver, the baseband output in the I/Q channel canbe represented as B(t) and the quadrature of B(t). Meanwhile, the Besselcoefficient with a negative index number and a positive index number inequation (1) can be combined using the property: Jn(x)=J−n(x) for evennumbers of n and Jn(x)=−J−n(x) for odd numbers of n. Therefore, thebaseband I/Q output can be represented as:

$\begin{matrix}\begin{matrix}{{I(t)} = {\cos\left( {\frac{4\pi\;{x_{h}(t)}}{\lambda} + \frac{4\pi\;{x_{r}(t)}}{\lambda} + \phi} \right)}} \\{= {{{{- {2\left\lbrack {{C_{10}{\sin\left( {\omega_{r}t} \right)}} + {C_{01}{\sin\left( {\omega_{h}t} \right)}} + \ldots} \right\rbrack}} \cdot \sin}\;\phi} +}} \\{{{2\left\lbrack {{C_{20}{\cos\left( {2\omega_{r}t} \right)}} + {C_{02}{\cos\left( {2\omega_{h}t} \right)}} + \ldots} \right\rbrack} \cdot \cos}\;\phi\mspace{14mu}\left( {2a} \right)}\end{matrix} & \left( {2.a} \right) \\\begin{matrix}{{Q(t)} = {\sin\left( {\frac{4\pi\;{x_{h}(t)}}{\lambda} + \frac{4\pi\;{x_{r}(t)}}{\lambda} + \phi} \right)}} \\{= {{{{+ {2\left\lbrack {{C_{10}{\sin\left( {\omega_{r}t} \right)}} + {C_{01}{\sin\left( {\omega_{h}t} \right)}} + \ldots} \right\rbrack}}\; \cdot \cos}\;\phi} +}} \\{{{2\left\lbrack {{C_{20}{\cos\left( {2\omega_{r}t} \right)}} + {C_{02}{\cos\left( {2\omega_{h}t} \right)}} + \ldots} \right\rbrack} \cdot \sin}\;\phi\mspace{14mu}\left( {2b} \right)}\end{matrix} & \left( {2.b} \right)\end{matrix}$

where Cij=Ji(4πmr/λ)·Jj(4πmh/λ) determines the amplitude of everyfrequency component. The ellipses in equation (2) represent higher orderodd and even harmonics.

From equation (2), the ratio of cos φ and sin φ determines the relativestrength between the even order and the odd order harmonics. Therefore,the optimal/null detection point is determined by the residue phase φ.For example, when φ is close to 90°, the fundamental frequency ofrespiration and heartbeat signals dominates in the I channel while thesecond order harmonic of desired signals dominates in the Q channel,thus I is close to the optimal detection point and Q is close to thenull detection point. According to the single-beam model, when eitherone of the two quadrature channels is close to an optimal detectionpoint, the other one should be close to the null detection point.

A. Complex Signal Demodulation

It is important to note that although the frequency of wavelengths λ₁and λ₂ are described as substantially similar, these frequencies can beselected to be very close to each other. The lack of a phase-lock loopcoupled between transceivers 102 and 112 is the cause for this slightdifference.

Two free running VCOs are used for the two transmitters so that λ₁ andλ₂ are close to each other but always have a slight difference becausethe system does not incorporate any phase-locked-loop. This provides thefollowing advantages: Firstly, the signal from one transceiver can beeasily rejected by the other transceiver, because the slight differencein radio frequency results in a large difference in baseband signal forvital sign detection and can easily filter out signal from the othertransceiver. Secondly, since λ₁ and λ₂ are set very close, it enablesthe movement cancellation method to cancel out Doppler frequency shiftdue to random body movement, as will be shown theoretically in thefollowing. Finally, the free running VCO without phase-locked-loopsignificantly reduces the complexity and cost of this technique.

Referring now to FIG. 2 shown is a block diagram illustrating: (a) acomplex signal demodulation; and (b) an arctangent demodulation,according to the present invention. The complex signal demodulation ofFIG. 2( a) can eliminate the optimum/null detection point problem bycombining the I and Q signals in baseband. As shown in Equation 2 (a),the complex signal is software-reconstructed in real time as:

$\begin{matrix}\begin{matrix}{{S(t)} = {{I(t)} + {j \cdot {Q(t)}}}} \\{= {\exp\left\{ {j\left\lbrack {\frac{4\;\pi\;{x_{h}(t)}}{\lambda} + \frac{4\pi\;{x_{r}(t)}}{\lambda} + \phi} \right\rbrack} \right\}}} \\{= {{{+ 2}{{j\left\lbrack {{C_{10}{\sin\left( {\omega_{r}t} \right)}} + {C_{01}{\sin\left( {\omega_{h}t} \right)}} + \ldots} \right\rbrack} \cdot {\mathbb{e}}^{j\phi}}} +}} \\{{{2\left\lbrack {{C_{20}{\cos\left( {2\omega_{r}t} \right)}} + {C_{02}{\cos\left( {2\omega_{h}t} \right)}} + \ldots} \right\rbrack} \cdot {\mathbb{e}}^{j\phi}}\mspace{14mu}(3)}\end{matrix} & (3)\end{matrix}$

where e^(jφ) has a constant envelope of one, and thus the effect of φ onsignal amplitude can be eliminated. Applying the complex Fouriertransform to the signal S(t) for spectral analysis, the residual phase φwill not affect the relative strength between the odd order and the evenorder frequency components. The desired signal components (odd ordertones) will always be present in the spectrum.

Meanwhile, the DC components accumulated in the I and the Q channelsonly contribute to the DC term in the complex signal S(t), thus does notaffect obtaining the frequency of the desired signal component. Inpractice, the residual baseband DC components can be easily extracted asthe average of signals in every time-domain sliding window and thus besafely removed. As a result, the complex signal demodulation greatlysimplifies the demodulation procedure and is immune from DC offset whenno random body movement is present. However, the complex signaldemodulation is not able to completely eliminate the higher even orderharmonics.

For random body movement cancellation, measurements need to be performedfrom both sides of the human body. In this way, the signal detected fromthe two transceivers can be expressed as:

$\begin{matrix}{{S_{f}(t)} = {\exp\left\{ {j\left\lbrack {\frac{4\pi\;{x_{h\; 1}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 1}(t)}}{\lambda} + \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{1}} \right\rbrack} \right\}}} & \left( {4.a} \right) \\{{S_{b}(t)} = {\exp\left\{ {j\left\lbrack {\frac{4\pi\;{x_{h\; 2}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 2}(t)}}{\lambda} - \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{2}} \right\rbrack} \right\}}} & \left( {4.b} \right)\end{matrix}$

where x_(h1)(t) and x_(r1)(t) are the heartbeat-induced and therespiration-induced physiological movements on the front chest wall,x_(h2)(t) and x_(r2)(t) are the heartbeat-induced and therespiration-induced physiological movements on the back, φ₁, φ₂ are theresidual phase of the transceivers in front of the body and behind thebody, and y(t) is the random body movement. Note that the pairs ofphysiological movements on both sides of the body, e.g. x_(h1)(t) andx_(h2)(t), move in the same direction relative to their respectivedetecting radar. On the other hand, when the body is drifting toward oneof the radars, it is moving away from the other one. Therefore, bymultiplying S_(f)(t) and S_(b)(t), the y(t) term in the baseband outputS_(fb)(t)=S_(f)(t)·S_(b)(t) will be cancelled out, while thephysiological movement terms are enhanced:

$\begin{matrix}{{S_{f\; b}(t)} = {\exp\left\{ {j\left\lbrack {\frac{4{\pi\left\lbrack {{x_{h\; 1}(t)} + {x_{h\; 2}(t)}} \right\rbrack}}{\lambda} + \frac{4{\pi\left\lbrack {{x_{r\; 1}(t)} + {x_{r\; 2}(t)}} \right\rbrack}}{\lambda} + \phi_{1} + \phi_{2}} \right\rbrack} \right\}}} & (5)\end{matrix}$

The above operation can also be interpreted as convolution and frequencyshift in frequency domain, thus canceling the Doppler frequency driftand keeping only the periodic Doppler phase effects.

Although it is shown that the complex signal demodulation itself doesnot require the baseband DC offset information, the performance ofrandom body movement cancellation is affected by the DC offset. Properestimation or calibration of the DC offset is beneficial for successfulcancellation of the noise due to random body movement.

B. Arctangent Demodulation

Referring now to FIG. 2( b) shown is block diagram of arctangentdemodulation, according to the present invention Another way toeliminate the optimum/null detection point problem in the quadraturedemodulation system is to use arctangent demodulation ˜0 by calculatingthe total Doppler phase shift. Its block diagram is shown in FIG. 2 (b).This is described further in the publication by B. Park, O.Boric-Lubecke, and V. M. Lubecke, “Arctangent demodulation with DCoffset compensation in quadrature Doppler radar receiver systems”, IEEETrans. Microwave Theory and Techniques, vol. 55, pp. 1073-1079, May2007, which is hereby incorporate by reference in its entirety. Takinginto account the phase discontinuity when the signal trajectory crossesthe boundary of two adjacent quadrants, the arctangent demodulationcalculates the total angular information ψ(t) as:

$\begin{matrix}{{\psi(t)} = {{{\arctan\frac{Q(t)}{I(t)}} + F} = {\frac{4\pi\;{x_{h}(t)}}{\lambda} + \frac{4\pi\;{x_{r}(t)}}{\lambda} + \phi}}} & (6)\end{matrix}$

where F is a multiple of 180° for the purpose of eliminating thediscontinuity when ψ(t) crosses the boundary of two adjacent quadrantsin the constellation graph.

Because ψ(t) is a linear combination of the desired signal x_(h)(t) andx_(r)(t), the information of the vital signs can be obtained with thenonlinear phase modulation effect eliminated. The advantage is theability to eliminate the harmonic and intermodulation interference.However, previous demonstration of this embodiment accurate calibrationof the DC offset is needed in order to properly reconstruct the angularinformation. This is described further in the publication by B. Park, O.Boric-Lubecke, and V. M. Lubecke, “Arctangent demodulation with DCoffset compensation in quadrature Doppler radar receiver systems”, IEEETrans. Microwave Theory and Techniques, vol. 55, pp. 1073-1079, May2007, which is hereby incorporate by reference in its entirety.

The difficulty of accurate DC offset calibration encountered in Dopplerradar vital sign detection is that the DC offset is not only produced bythe electronic circuit, but also by the unmodulated reflected signal,i.e. signal reflected from stationary objects and other parts of thehuman body rather than the moving chest wall. Therefore, the DC offsetchanges as the environment changes and needs to be calibrated when it ischanged. Again, this is described further in the publication by B. Park,O. Boric-Lubecke, and V. M. Lubecke, “Arctangent demodulation with DCoffset compensation in quadrature Doppler radar receiver systems”, IEEETrans. Microwave Theory and Techniques, vol. 55, pp. 1073-1079, May2007, which is hereby incorporate by reference in its entirety.

On the other hand, the presence of baseband DC offset results in ashifted trajectory in the constellation graph. Although the angularinformation ψ(t) will be changed significantly when the trajectory isshifted, the angular movement is still periodic. This implies that whenanalyzing the spectrum of ψ(t) in the presence of a DC offset, thedesired frequency components still exist. The difference observed in thespectrum is a changed harmonic level. Therefore, if the DC offset can beproperly estimated, it is still possible to extract the desired vitalsigns. As will be demonstrated in Section V, a trajectory-fittingprocedure is adopted in this paper for DC offset estimation in baseband.Experiments will show that this procedure can be used for vital signdetection in the absence of random body movement.

When random body movement is present, the angular information recoveredfrom the front (ψ_(f)) and the back (ψ_(b)) of the human body can beexpressed as:

$\begin{matrix}{{\psi_{f}(t)} = {\frac{4\pi\;{x_{h\; 1}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 1}(t)}}{\lambda} + \frac{4\pi\;{y(t)}}{\lambda} + \phi_{1}}} & \left( {7.a} \right) \\{{\psi_{b}(t)} = {\frac{4\pi\;{x_{h\; 2}(t)}}{\lambda} + \frac{4\;\pi\;{x_{r\; 2}(t)}}{\lambda} - \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{2}}} & \left( {7.b} \right)\end{matrix}$

where x_(h1)(t), x_(r1)(t), x_(h2)(t), x_(r2)(t), φ₁, and φ₂ are thesame as defined in Section II-A. Instead of multiplying the two signalsas in the case of using complex signal demodulation, the random bodymovement can be cancelled out by adding the angular information ofequations (7.a) and (7.b) together to obtainψ_(fb)(t)=ψ_(f)(t)+ψ_(b)(t):

$\begin{matrix}{{\psi_{f\; b}(t)} = {\frac{4{\pi\left\lbrack {{x_{h\; 1}(t)} + {x_{h\; 2}(t)}} \right\rbrack}}{\lambda} + \frac{4{\pi\left\lbrack {{x_{r\; 1}(t)} + {x_{r\; 2}(t)}} \right\rbrack}}{\lambda} + \phi_{1} + \phi_{2}}} & (8)\end{matrix}$

III. EFFECTS OF PHASE OFFSET

Since a real antenna with a certain radiation pattern does not haveinfinite directivity, signals are reflected and captured from differentparts of the body. When signals on different paths with differentintensity and residual phases are received by the radar, they are simplysummed together by the receiving antenna, either canceling out orenhancing the desired signal components. Therefore, a ray-tracing modelis developed to compensate for the shortage of the single-beam model.This is described further in the publication by C. Li, Y. Xiao, and J.Lin, “Design Guidelines for Radio Frequency Non-contact Vital SignDetection,” Proceedings of the 29th Annual International Conference ofthe IEEE EMBS, pp. 1651-1654, Lyon, France, Aug. 23-26, 2007, which ishereby incorporate by reference in its entirety.

FIG. 3 is a graph illustrating: (a) ray-tracing model and the angularinformation (b) ray-tracing model of signals reflected from point A andB on the body using a 5.8 GHz radar, according to the present invention.The antenna is facing the body in the −Z direction of the X-Y-Zcoordinate. As shown in FIG. 3( a), the actual received signal should berepresented from a ray-tracing point of view as:

$\begin{matrix}{{I(t)} = {\int{\int\limits_{s}{{{E\left( {x,y} \right)} \cdot {\cos\left\lbrack \left\lbrack {\phi + {\frac{4\pi}{\lambda}\left\{ {{\rho\left( {x,y} \right)}^{2} + \mspace{256mu}\mspace{146mu}\left\lbrack {d_{0} + {{m_{h}\left( {x,y} \right)}\sin\left( {\omega_{h}t} \right)} + {{m_{r}\left( {x,y} \right)}{\sin\left( {\omega_{r}t} \right)}}} \right\rbrack^{2}} \right\}^{1/2}}} \right\rbrack \right\rbrack}} \times {\mathbb{d}s}}}}} & \left( {9.a} \right) \\{{Q(t)} = {\int{\int\limits_{s}{{{E\left( {x,y} \right)} \cdot {\sin\left\lbrack \left\lbrack {{\Delta\phi} + \frac{4\pi}{\lambda} + \left\{ {{\rho\left( {x,y} \right)}^{2} + \mspace{211mu}\mspace{149mu}\left\lbrack {d_{0} + {{m_{h}\left( {x,y} \right)}{\sin\left( {\omega_{h}t} \right)}} + {{m_{r}\left( {x,y} \right)}{\sin\left( {\omega_{r}t} \right)}}} \right\rbrack^{2}} \right\}^{1/2}} \right\rbrack \right\rbrack}} \times {{\mathbb{d}s}.}}}}} & \left( {9.b} \right)\end{matrix}$

Assume the antenna is placed 1 m away in front of the heart center, andthe locations of the heart center A and the body center B on the frontchest wall are separated by 11 cm. The difference in the transmissionpath for signals from the antenna to the two points is Δx=√{square rootover (1²+0.11²)}−1=0.006 m, which is replicated in the receiving pathand would produce a phase difference of 83.5 degree for a 5.8 GHz radar.Meanwhile, the radiation intensity of the antenna on the body surface isdifferent from point to point, depending on the antenna radiationpattern. This implies that the received baseband signals from the twopoints will have different locations and movement patterns in theconstellation graph, as shown in FIG. 3( b). Therefore, the real casefor vital sign detection using complex signal demodulation andarctangent demodulation is complicated by the phase offset. Numericalsimulations are needed and will be presented in the following section.

IV. SIMULATION

Simulations have been performed based on the ray-tracing model. The twodemodulation techniques were applied to vital sign detection in thepresence/absence of random body movement.

A. Ray-tracing Model

FIG. 4. is a ray-tracing model illustrating: (a) the phase offset on thesurface of human body radiated by a 5.8 GHz radar; (b) a 7 by 7 elementsantenna array's radiation intensity on the human body; (c) approximationof the normalized amplitude of body movement caused by respiration; and(d) approximation of the normalized amplitude of body movement caused byheartbeat, according to the present invention The body model for asubject of 1.8 m height is shown in FIG. 4. Assuming the antenna is 1 min front of the heart center, the phase offset in different pathscompared with the beam propagating to the center of the heart is shownin FIG. 4( a) for a 5.8 GHz radar sensor. Dramatic change in phaseoffset is observed. Shown in FIG. 4( b) is the radiation intensity onthe human body produced by an ideal 7 by 7 antenna array comprised ofomnidirectional antennas spaced by λ/2. FIGS. 4( c) and (d) are theapproximation of the normalized amplitude of body movements caused byrespiration and heartbeat, respectively. It can be inferred that when acarrier frequency of 24 GHz is used for the higher sensitivity atshorter wavelengths, the phase change will be more significant.

B. Demodulation without Random Body Movement

To demonstrate the properties of the two demodulation techniques,numerical simulations were first performed without random body movementpresent. Two examples are presented, i.e. a 5.8 GHz quadrature radar,and a 24 GHz quadrature radar. Three types of signals were recorded andanalyzed.

Case I: a single-beam signal projected to the heart center, i.e. point Ain FIG. 3. This is the case analyzed by the single-beam model.

Case II: a single-beam signal projected to the body center, i.e. point Bin FIG. 3. In this case, respiration signal was picked up whileheartbeat signal is almost absent.

Case III: the actual signal transmitted and received by the radar.

It should be noted that only Case III can be realized in the laboratory.Case I and II analyze signals carried by a hypothetical single beamradiated by an antenna with a very high directivity radiation pattern.

Example I 5.8 GHz Quadrature Radar

Simulation results are shown in FIG. 5 for detection from the back ofthe human body. More specifically FIG. 5 is a graph of demodulation fora 5.8 GHz radar illustrating: (a) a signal detected at heart center(Case I) and at body center (Case II); (b) an actual received signal(Case III); (c) an angular information ψ(t) of the received signal; and(d) baseband spectra obtained by the complex signal demodulation and thearctangent demodulation (the DC component is not shown in the basebandspectrum). The residual phase produced in the electronic circuit wasassumed to be 0°, which means the Q channel was at the optimum detectionpoint while the I channel was at the null detection point according tothe single-beam model.

FIGS. 5( a) and (b) show the signal trajectories in the constellationgraph. As predicted in Section II, signals reflected from differentparts of the human body are affected by two variations: the phase offsetand the radiation intensity. The former variation embodies itself asdifferent angles of the trajectory shown in FIG. 5( a), while the latteris demonstrated as different radii of the trajectory. As a result, whenthe receiver receives the vital sign signals, which is the superpositionof all the signals reflected from different parts of the body, the totalreceived signal trajectory is deformed from an ideal circle, as shown inFIG. 5( b). It should be noted that the constellation deformation is notcaused by noise, which was not included in simulation.

Although the foregoing discussions and simulation results appearundesirable, the recovered angular information based on equation (6) isnonetheless periodic and not seriously disturbed by the phase offsetproblem, as shown in FIG. 5( c). The spectrum of the complex signal andthe recovered angular information were analyzed and plotted in FIG. 5(d). Although the detection was made with one channel at the nulldetection point and the other at the optimum detection point, both ofthe two demodulation techniques can successfully identify therespiration and heartbeat components.

Therefore, the complex signal demodulation and the arctangentdemodulation for 5.8 GHz radar system are demonstrated to be effectivesolutions to achieve reliable detection and eliminate the null detectionpoint problem.

Example II 24 GHz Quadrature Radar

In this example, the carrier frequency was 24 GHz and the residual phaseproduced in the electronic circuit was assumed to be 450, which meansthe detection was performed at the middle between the null and theoptimum detection points. The constellation plots are shown in FIGS. 6(a) and (b). More specifically, FIG. 6 is a graph of demodulation for a24 GHz radar illustrating: (a) a signal detected at heart center (CaseI) and at body center (Case II); (b) an actual received signal (CaseIII), with the recovered angular information shown in inset; (c) abaseband spectra obtained by the complex signal demodulation and thearctangent demodulation (DC component not shown in the spectra),according to the present invention. The complex signal demodulationcauses harmonic (H₂) and intermodulation (Int) interference. Due to fastvariation of the phase offset on the surface of the human body, moresevere trajectory deformation was observed. However, angular informationrecovered from (6) is still periodic, as shown in the inset of FIG. 6(b). The baseband spectra from the two demodulation techniques are shownin FIG. 6( c). Again, the respiration and the heartbeat components canbe identified from the spectrum by using both of the techniques.

Furthermore, the result in FIG. 6( c) verifies that the arctangentdemodulation can eliminate the harmonics and intermodulation termscaused by the nonlinear phase modulation effect, making the spectrumcleaner than that obtained by complex signal demodulation.

Another phenomenon to be noted is the optimum/null detection ambiguity.FIG. 7 is a graph of a baseband spectrum detected by the I and the Qchannels with a carrier frequency of 24 GHz illustrating: (a) a spectrumof a single-beam signal projected to the heart center; and (b) aspectrum of the actually received signal, according to the presentinvention. Shown in FIG. 7( a) is the baseband spectrum of the I and theQ channels in Case I, i.e. the spectrum of the single beam signalprojected to the center of the heart. The peaks of the respiration andthe heartbeat components in I channel have the same amplitudes as thosein Q channel, which is in accordance with (2) predicted by thesingle-beam model since the detection was performed at the mid-pointbetween the null and the optimum detection points. However, the basebandspectrum of the actual received signal, as shown in FIG. 7( b), showsthat the I and the Q channels have significant differences in theheartbeat signal strength. While the I channel preserves the heartbeatsignal, the Q channel shows strong harmonic and intermodulationcomponents. This is because of the enhancement and cancellation amongsignals with different phase offsets. And it demonstrates the necessityof effectively combining the two channels even when the detection is notcarried out at the null detection point.

C. Random Body Movement Cancellation

The random body movement cancellation technique was also simulated usingthe ray-tracing model with a carrier frequency of 5.8 GHz. The randomroaming of the body was fully modeled in three dimensions (X, Y, and Z)which are defined in FIG. 3. Typically the subject under test has largerrandom body movements in two dimensions than the third dimension, e.g.the horizontal movements in the X and Z directions are more obvious thanthe vertical movement in the Y direction for a seated person. Therefore,the time-variant velocity of random body movement was modeled as uniformdistribution between 0 and a maximum value of 4 mm/s in the X and the Zdirections. And the amplitude of random body movement in the Y directionwas modeled as 0.1 of that in the other two directions.

Turning now to FIG. 8. is a graph of baseband spectra obtained whenrandom body movement is present illustrating: (a) the random bodymovement is shown in the Z, X, and Y directions, which are defined inFIG. 3; and (b) a baseband spectra by arctangent demodulation (AD) andcomplex signal demodulation (CSD), according to the present invention.The movement components in each direction are shown in FIG. 8 (a), andthe baseband spectra detected from the front and the back using the twodemodulation techniques are shown in FIG. 8( b). When random bodymovement is present, the desired respiration and heartbeat signalcomponents will be overwhelmed by the noise generated by random bodymovement.

FIG. 9. is a graph of illustrating: (a) angular information and basebandspectrum; and (b) angular information recovered by random body movementcancellation (RBMC) using the two demodulation techniques; accurate DCinformation is used in demodulation but not shown in the spectrum,according to the present invention. If the system can successfullypreserve the DC offset information up to the baseband output, therecovered baseband angular information and the spectra obtained byrandom body movement cancellation were simulated and shown in FIG. 9.The respiration and heartbeat components were successfully recovered byboth demodulation techniques, which showed similar performance inrecovering the desired signal components. It should be noted thatalthough the random body movement can exist in the directionperpendicular to the radar direction, this technique still worksreliably because only the movement in the radar direction is criticalfor the detection.

If the DC offset cannot be perfectly preserved up to the basebandoutput, however, the performance of random body movement cancellationbased on both of the demodulation techniques deteriorates.

FIG. 10. is a graph illustrating: (a) angular information and basebandspectrum; and angular information recovered from the random bodymovement cancellation (RBMC) technique; the random body movements aremodeled in three dimensions, and the DC offset in each transceiver is30% of the maximum signal amplitude, according to the present invention.More specifically, shown in FIG. 10 is an example when DC offset waspresent at the baseband output of the two transceivers but notaccurately preserved. For each transceiver, the baseband DC offsetlevels were modeled to be the same in the I/Q channels and were 30% ofthe maximum signal amplitude. In the simulation, the above DC offsetlevel was subtracted from the ideal I and Q channel signals. Then, bothdemodulation techniques were applied to cancel out the random bodymovement. It is shown that the complex signal demodulation can stillidentify the respiration and heartbeat components, but the arctangentdemodulation is unable to recover the heartbeat signal. The reason forthis disadvantage of using arctangent demodulation in random bodymovement cancellation is, as shown in (8), the cancellation is based onthe linear combination of the calculated phase, which is stronglyaffected by the location of the constellation origin.

V. EXPERIMENT

Experiments have been performed in the laboratory to verify the theoryand compare the performance of the two demodulation techniques forrandom body movement cancellation. For consumer applications of thistechnique, it is desirable to have portable radars that can detect vitalsigns from several meters away, radiate a power of lower than 0 dBm, andhave all the hardware integrated together at an affordable price.Therefore, 4-7 GHz portable radar was designed for this purpose. Theradar integrates the quadrature transceiver, the two-stage basebandamplifier, and the power management circuit on a single printed circuitboard (Rogers RO4350B substrate) with a size of 6.8×7.5 cm². FIG. 11 isa block diagram of the 4-7 GHz radar transceiver as shown in FIG. 1,according to the present invention. The specifications and manufacturersof the radio frequency components are listed in Table I. For researchpurpose, four voltage controlled oscillators (VCO) were implemented fora wide tuning range to obtain different optimal carrier frequenciesunder different environments. This is further described in thepublication by Li, and J. Lin, “Optimal Carrier Frequency of Non-contactVital Sign Detectors,” Proceedings of IEEE Radio and Wireless Symposium,pp. 281-284, Long Beach, Jan. 9-11, 2007, which is hereby incorporate byreference in its entirety. When a specific application is known, onlyone VCO is needed and the SP4T switch can be eliminated to furtherreduce the cost.

TABLE I BUILDING BLOCKS AND SPECIFICATIONS USED IN 4-7 GHz RADAR BlockManufacturer Specifications VCO1 Hittite 4.45-5.0 GHz, −105 dBc/Hz @100kHz phase noise, 4 dBm output power VCO2 Hittite 5.0-5.5 GHz, −103dBc/Hz @100 kHz phase noise, 2 dBm output power VCO3 Hittite 5.5-6.1GHz, −102 dBc/Hz @100 kHz phase noise, 2 dBm output power VCO4 Hittite6.1-6.72 GHz, −101 dBc/Hz @100 kHz phase noise, 4.5 dBm output powerSwitch Hittite DC-8 GHz, 40 dB isolation @6 GHz, 1.8 dB insertion loss@6 GHz, SP4T Gain RFMD DC-8 GHz, 15.5 dB maximum gain, Block 14.5 dBm P1dB @6 Ghz Mixer Hittite 4-8.5 GHz, 50 dB LO to RF isolation, 40 dB imagerejection LNA Hittite 3.5-7.0 GHz, 16 dB gain, 2.5 dB NF

Since the vital sign has a frequency less than several Hertz, largecoupling capacitors C1 and C2 of 10 μF were used to isolate the DCvoltages of the mixer output and baseband amplifier input. Because the10 μF coupling capacitors block the DC signal in addition to isolatingDC voltages of two different circuits, no DC information was recordedduring the measurement. The coupling capacitor (C1, C2=10 μF) and thebaseband amplifier input resistor (R1, R2=160 kΩ) were chosen such thatfor a heartbeat signal with a frequency around 1 Hz, the voltage drop onthe capacitor is no more than 1/10 of the signal amplitude. This leadsto a time constant of approximately 1.6 seconds, which means that in thereal-time signal processing software, a 2 second initiation time isneeded.

For random body movement cancellation, measurements were performed bytwo identical radars. In this embodiment patch antenna arrays withorthogonal polarization were installed in the two transceivers toeliminate the interference between the two units. An example of theseradars is further described in the publication by C. Li, and J. Lin,“Complex Signal Demodulation and Random Body Movement CancellationTechniques for Non-contact Vital Sign Detection,” IEEE MTT-SInternational Microwave Symposium Digest, June, 2008, which is herebyincorporated by reference in its entirety. It was observed in theexperiment that the antenna gain should be higher than 4 dB for theradar to have a good signal-to-noise ratio from up to 2 m away. Theantenna was designed to have a maximum directivity gain of 9 dB atbroadside, so that the vital signs of the subject in front of theantenna will be picked up. Free-running VCOs were used for the twotransmitters so that the actual carrier wavelengths were close to eachother but always had a slight difference in the absence of aphase-locked-loop. As a result, the signal from one transceiver wasfurther rejected by the other transceiver in the baseband because thesmall difference in the carrier frequency results in a large differencein baseband frequency compared to the vital sign frequencies. The phasenoise reduction due to range correlation makes the free-running VCOadequate for vital sign detection. This is further described in thepublication by A. D. Droitcour, O. Boric-Lubecke, V. M. Lubecke, J. Lin,and G. T. A. Kovac, “Range correlation and I/Q performance benefits insingle-chip silicon Doppler radars for noncontact cardiopulmonarymonitoring,” IEEE Trans. Microwave Theory and Techniques, vol. 52, pp.838-848, March 2004, which is hereby incorporate by reference in itsentirety along with the publication by M. C. Budge, Jr. and M. P. Burt,“Range correlation effects on phase and amplitude noise,” Proc. IEEESoutheastcon, Charlotte, N.C., 1993, pp 5, which is hereby incorporatedby reference in its entirety.

To reduce the hardware cost and the requirement of signal processingspeed, the amplified baseband signals were sampled by a 12-bitmultifunction data acquisition module (NI USB-6008) with a low samplingrate of 20 Hertz, which is fast enough for the vital sign signal oftypically less than 2.5 Hertz. The sampled data were fed into a laptopfor real-time signal processing by LabVIEW. The sampling rate andresolution make it possible to implement the baseband signal processingin a low cost DSP microchip such as the TI C2000 family digital signalcontrollers for various applications in the future.

To focus on the properties of demodulation and random body movementcancellation techniques, no baseband filtering was implemented in eitherhardware or software. All the results presented are based on theoriginal baseband signal.

A. DC Offset Estimation in Baseband

Because of the coupling capacitor in the radar between the receiveroutput and the baseband amplifier input and the variability of DC offsetwithin the experimental environment, it is relatively difficult toaccurately calibrate out the DC offset of the whole system. Instead, theDC offset was estimated by fitting the signal trajectory into a propersegment of circle in the constellation graph.

Turning now to FIG. 12 is a graph of DC offset estimation illustrating:(a) a trajectory of detected baseband signal with no DC information andwith estimated DC offset level added; and (b) a spectra obtained by thetwo demodulation techniques. Signal with estimated DC offset added wasused for arctangent demodulation. For example, FIG. 12( a) shows theconstellation graph of the baseband signal detected from the back of thehuman body when no random body movement was present. Because of theabsence of DC information, the original signal trajectory was located atthe center of the constellation graph. After adding an estimated DCoffset level of 0.8 V for both I and Q channels in the baseband, thetrajectory was fitted into a circle. FIG. 12( b) shows the basebandspectra obtained by the complex signal demodulation and the arctangentdemodulation. As shown in both theory and experiment in, the DC offsetdoes not affect complex signal demodulation when random body movement isabsent. This is described further in the publication by C. Li, and J.Lin, “Complex Signal Demodulation and Random Body Movement CancellationTechniques for Non-contact Vital Sign Detection,” IEEE MTT-SInternational Microwave Symposium Digest, June, 2008, which is herebyincorporate by reference in its entirety. Therefore, the spectrumobtained by complex signal demodulation can be used as a reference toevaluate the reliability of arctangent demodulation using the estimatedDC offset information. The spectra of FIG. 12( b) match well with eachother, showing that the baseband DC offset estimation method is accurateenough for arctangent demodulation when no random body movement ispresent. Based on this DC offset estimation method, estimated DC offsetswere added to original detected data and used for random body movementcancellation. However, it should be noted that in the presence of therandom body movement, the DC information produced by the reflection fromthe bulk of the body always changes. Therefore, it is impossible todynamically obtain the precise DC offset information of the overallsystem: no matter whether the DC offset is calibrated out using themethod proposed in the publication by B. Park, O. Boric-Lubecke, and V.M. Lubecke, “Arctangent demodulation with DC offset compensation inquadrature Doppler radar receiver systems”, IEEE Trans. Microwave Theoryand Techniques, vol. 55, pp. 1073-1079, May 2007, which is herebyincorporated by reference in its entirety, or estimated by the signaltrajectory fitting method of this paper, there will always be DCinformation error when the body position changes. And it is of greatinterest to compare in real experiments that how robust the twodemodulation techniques are in the presence of the inevitable DC offseterror.

B. Random Body Movement Cancellation

During the experiment, the subject under test was gently changingposition in a chair, so that the noise of random body movement wasemphasized. FIG. 13 is a graph of signals detected from: (a) the frontof a human body; and (b) the back of the human body, according to thepresent invention. Further, FIG. 13 shows the time domain signaldetected from the front and the back of the human body when random bodymovement was present. Since the physiological movement caused byrespiration and heartbeat has larger amplitude on the front chest wallthan on the back, the signal detected from the back is more severelyaffected by the random body drift. Note that the detected signalamplitude shown in FIG. 13 does not reflect the real physiologicalmovement amplitude, since other factors such as distance and basebandamplifier gain also affect the signal level. For example, in theexperiment, the baseband amplifier gain of the radar detecting from theback is 3 dB higher than the other one detecting from the front. The twodemodulation techniques were used to cancel out random body movement torecover the desired signal.

The estimation based on signal trajectory fitting was used here forarctangent demodulation. FIG. 14 is a graph of random body movementcancellation using arctangent demodulation illustrating: (a) a spectrameasured from the front and the back of the human body; (b) a spectrumfrom combining the two transceiver outputs, the heartbeat informationcannot be recovered due to inaccurate DC offset information, accordingto the present invention.

The baseband spectra detected from the front and the back of the humanbody are shown in FIG. 14( a). The angular information from the twotransceivers was combined as described in Section II-B, and theresulting baseband spectrum is shown in FIG. 14( b). Due to theinaccuracy of DC offset estimation, the combined spectrum failed torecover the desired heartbeat signal component.

On the other hand, the same signals have been processed by the complexsignal demodulation. FIG. 15 is a graph of random body movementcancellation using complex signal demodulation illustrating: (a) aspectra measured from the front and the back of the human body; and (b)an output spectrum by the random body movement cancellation technique,the heartbeat information is recovered, according to the presentinvention. This is further described in the publication by C. Li, and J.Lin, “Complex Signal Demodulation and Random Body Movement CancellationTechniques for Non-contact Vital Sign Detection,” IEEE MTT-SInternational Microwave Symposium Digest, June, 2008, which is herebyincorporated by reference in its entirety.

FIG. 15( a) shows the baseband spectra of the complex signal detectedfrom the front and the back of the human body. Since the physiologicalmovement at the back is weaker than that at the front chest wall, thenoise completely overwhelmed the physiological signals from the back andonly overwhelmed the heartbeat signal from the front. When the techniquedescribed in Section II-A was applied to combine the signals detectedfrom the front and the back of the human body, the heartbeat signal wassuccessfully recovered as shown in FIG. 15( b).

The above comparative study verifies the simulation in Section IV-C thatthe complex signal demodulation is more favorable in random bodymovement cancellation when the DC offset at baseband output cannot beaccurately determined.

VI. CONCLUSION AND NON-LIMITING EXAMPLES

Simulations and experiments have been performed to demonstrate thecomplex signal demodulation and the arctangent demodulation for randombody movement cancellation in Doppler radar vital sign detection. Thecomplex signal demodulation is easier to implement in that it does notneed an intermediate signal processing stage to recover the angularinformation, and it is robust when DC offset is present. The latterproperty also makes it more favorable for random body movementcancellation. On the other hand, the arctangent demodulation has theadvantage of eliminating the harmonic and intermodulation interferenceat high frequencies using high gain antennas. The effects ofconstellation deformation and optimum/null detection ambiguity caused bythe phase offset due to finite antenna directivity are also discussed.

The present invention can be realized in hardware, software, or acombination of hardware and software. A system according to a preferredembodiment of the present invention can be realized in a centralizedfashion in one computer system or in a distributed fashion wheredifferent elements are spread across several interconnected computersystems. Any kind of computer system—or other apparatus adapted forcarrying out the methods described herein—is suited. A typicalcombination of hardware and software could be a general purpose computersystem with a computer program that, when being loaded and executed,controls the computer system such that it carries out the methodsdescribed herein.

In general, the routines executed to implement the embodiments of thepresent invention, whether implemented as part of an operating system ora specific application, component, program, module, object or sequenceof instructions may be referred to herein as a “program.” The computerprogram typically is comprised of a multitude of instructions that willbe translated by the native computer into a machine-readable format andhence executable instructions. Also, programs are comprised of variablesand data structures that either reside locally to the program or arefound in memory or on storage devices. In addition, various programsdescribed herein may be identified based upon the application for whichthey are implemented in a specific embodiment of the invention. However,it should be appreciated that any particular program nomenclature thatfollows is used merely for convenience, and thus the invention shouldnot be limited to use solely in any specific application identifiedand/or implied by such nomenclature.

FIG. 16 is a flow diagram of the overall random body movementcancellation, according to the present invention. The process begins instep 1602 and immediately proceeds to step 1604 with sending on at leasttwo electromagnetic signals comprising a first electromagnetic signalwith a first frequency to a first side of a body from a firstelectromagnetic wave transceiver and a second electromagnetic signalwith a second frequency to a second side of a body from a secondelectromagnetic wave transceiver. Next in step 1606 these signals arereceived. In step 1608, a first baseband signal and a second basebandsignal are extracted out of the first electromagnetic signal and thesecond electromagnetic signal respectively in step 1610 a demodulationis carried out by mathematically combining the first baseband complexsignal with the second baseband complex signal to cancel out a Dopplerfrequency drift therebetween to yield a periodic Doppler phase effect.An optional step 1612 is performed for output the results to a devicesuch as a display, printer, buzzer, storage or wireless device and theprocess ends in step 1614.

FIG. 17 is a generalized block diagram 1700 of a computer system usefulfor implementing the noise cancellation algorithm according to thepresent invention. The mass storage interface 1708 is used to connectmass storage devices, such as data storage device 1716, to theinformation processing system 1700. One specific type of data storagedevice is a computer readable medium such as DASD drive 1716, which maybe used to store data to and read data from a CD 1718. The main memory1706 comprises the movement cancellation module 150, which has beendiscussed above in greater detail. Although illustrated as concurrentlyresident in the main memory 1706, it is clear that respectivecomponent(s) of the main memory 1706 are not required to be completelyresident in the main memory 1706 at all times or even at the same time.

Although only one CPU 1704 is illustrated for computer 1702, computersystems with multiple CPUs can be used equally effectively. Embodimentsof the present invention further incorporate interfaces that eachincludes separate, fully programmed microprocessors that are used tooff-load processing from the CPU 1704. Terminal interface 1710 is usedto directly connect one or more terminals 1720 to computer 1702 toprovide a user interface to the computer 1702. These terminals 1720,which are able to be non-intelligent or fully programmable workstations,are used to allow system administrators and users to communicate withthe information processing system 1700. The terminal 1720 is also ableto consist of user interface and peripheral devices that are connectedto computer 1702 and controlled by terminal interface hardware includedin the terminal I/F 1710 that includes video adapters and interfaces forkeyboards, pointing devices, and the like.

An operating system (not shown) included in the main memory is asuitable multitasking operating system such as the Linux, UNIX, Windows,operating system. Embodiments of the present invention are able to useany other suitable operating system. Some embodiments of the presentinvention utilize architectures, such as an object oriented frameworkmechanism, that allows instructions of the components of operatingsystem (not shown) to be executed on any processor located within theinformation processing system 1700. The network adapter hardware 1712 isused to provide an interface to the network 1722. Embodiments of thepresent invention are able to be adapted to work with any datacommunications connections including present day analog and/or digitaltechniques or via a future networking mechanism.

Although the exemplary embodiments of the present invention aredescribed in the context of a fully functional computer system, thoseskilled in the art will appreciate that embodiments are capable of beingdistributed as a program product via CD or DVD, e.g. CD 1718, CD ROM, orother form of recordable media, or via any type of electronictransmission mechanism.

Further, even though a specific embodiment of the invention has beendisclosed, it will be understood by those having skill in the art thatchanges can be made to this specific embodiment without departing fromthe spirit and scope of the invention. The scope of the invention is notto be restricted, therefore, to the specific embodiment, and it isintended that the appended claims cover any and all such applications,modifications, and embodiments within the scope of the presentinvention.

What is claimed is:
 1. A method for cancelling body movement effect fornon-contact vital sign detection, the method comprising: providing afirst transceiver and a second transceiver; simultaneously sending afirst electromagnetic signal with a first frequency to a first side of abody from the first transceiver and a second electromagnetic signal witha second frequency to a second side of a body from the secondtransceiver, wherein the second side of the body is an opposite side ofthe body from the first side of the body; receiving at least a firstreflected signal reflected back in response to the first electromagneticsignal via the first transceiver; extracting out a first basebandcomplex signal from the first reflected signal; receiving at least asecond reflected signal reflected back in response to the secondelectromagnetic signal via the second transceiver; extracting out asecond baseband complex signal from the second reflected signal;providing an apparatus for combining the first baseband complex signalwith the second baseband complex signal to cancel out a Dopplerfrequency drift therebetween to yield a periodic Doppler phase effect,wherein the apparatus comprises a processor and/or hardware; andcombining the first baseband complex signal with the second basebandcomplex signal to cancel out a Doppler frequency drift therebetween toyield a periodic Doppler phase effect via the apparatus.
 2. The methodof claim 1, further comprising: extracting at least one of respirationrate and heart rate from the periodic Doppler phase effect.
 3. Themethod of claim 2, further comprising: sending the at least one ofrespiration rate and heart rate from the periodic Doppler phase effectto a display.
 4. The method of claim 1, wherein the first frequency,corresponding to a first wavelength, λ₁, and the second frequency,corresponding to a second wavelength, λ₂, are close to each other suchthat (λ₁≈λ₂≈λ) and the combining the first baseband complex signal withthe second baseband complex signal comprises combining the firstbaseband complex signal with the second baseband complex signal viacomplex signal demodulation expressed by $\begin{matrix}{{S_{f}(t)} = {\exp\left\{ {j\left\lbrack {\frac{4\pi\;{x_{h\; 1}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 1}(t)}}{\lambda} + \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{1}} \right\rbrack} \right\}}} \\{{S_{b}(t)} = {\exp\left\{ {j\left\lbrack {\frac{4\pi\;{x_{h\; 2}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 2}(t)}}{\lambda} - \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{2}} \right\rbrack} \right\}}}\end{matrix}$ where x_(h1)(t) and x_(r1)(t) are heartbeat-induced andrespiration-induced physiological movements on the first side of thebody, x_(h2)(t) and x_(r2)(t) are heartbeat-induced andrespiration-induced physiological movements on the second side of thebody, φ₁ and φ₂ are residual phases of the first transceiver and thesecond transceiver, and y(t) is a body movement, wherein by multiplyingS_(f)(t) and S_(b)(t), the y(t) term in the baseband outputS_(fb)(t)=S_(f)(t)·S_(b)(t) will be cancelled out, while terms ofphysiological movement x_(h1)(t), x_(h2)(t), x_(r1)(t) and x_(h2)(t) areenhanced as expressed by${S_{f\; b}(t)} = {\exp{\left\{ {j\left\lbrack {\frac{4{\pi\left\lbrack {{x_{h\; 1}(t)} + {x_{h\; 2}(t)}} \right\rbrack}}{\lambda} + \frac{4{\pi\left\lbrack {{x_{r\; 1}(t)} + {x_{r\; 2}(t)}} \right\rbrack}}{\lambda} + \phi_{1} + \phi_{2}} \right\rbrack} \right\}.}}$5. The method of claim 4, wherein a DC offset in at least one of thefirst baseband complex signal and the second baseband complex signal isnot accurately calibrated.
 6. The method of claim 1, wherein the firstfrequency, corresponding to a first wavelength, λ₁, and the secondfrequency, corresponding to a second wavelength, λ₂, are close to eachother such that (λ₁≈λ₂≈λ) and the combining the first baseband complexsignal with the second baseband complex signal comprises combining thefirst baseband complex signal with the second baseband complex signalvia arctangent demodulation expressed by $\begin{matrix}{{\psi_{f}(t)} = {\frac{4\pi\;{x_{h\; 1}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 1}(t)}}{\lambda} + \frac{4\pi\;{y(t)}}{\lambda} + \phi_{1}}} \\{{\psi_{b}(t)} = {\frac{4\pi\;{x_{h\; 2}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 2}(t)}}{\lambda} - \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{2}}}\end{matrix}$ where x_(h1)(t) and x_(r1)(t) are heartbeat-induced andrespiration-induced physiological movements on the first side of thebody, x_(h2)(t) and x_(r2)(t) are heartbeat-induced andrespiration-induced physiological movements on the second side of thebody, φ₁ and φ₂ are residual phases of the first transceiver and thesecond transceiver, and y(t) is a body movement, wherein by addingψ_(f)(t) and ψ_(b)(t), the y(t) term in the baseband outputψ_(fb)(t)=ψ_(j)(t)+ψ_(b)(t) will be cancelled out, while terms ofphysiological movement x_(h1)(t), x_(h2)(t), x_(r1)(t) and x_(h2)(t) areenhanced and expressed by:${\psi_{f\; b}(t)} = {\frac{4{\pi\left\lbrack {{x_{h\; 1}(t)} + {x_{h\; 2}(t)}} \right\rbrack}}{\lambda} + \frac{4{\pi\left\lbrack {{x_{r\; 1}(t)} + {x_{r\; 2}(t)}} \right\rbrack}}{\lambda} + \phi_{1} + {\phi_{2}.}}$7. The method of claim 6, wherein a DC offset in the first baseband andthe second baseband is accurately calibrated.
 8. The method of claim 1,wherein the first transceiver and the second transceiver are selectedfrom the group consisting of: a 5.8 GHz quadrature radar transceiver;and a 24 GHz quadrature radar transceiver.
 9. A system for cancellingbody movement effect for non-contact vital sign detection, the systemcomprising: at least a first transceiver and at least a secondtransceiver, wherein the system is configured such that the firsttransceiver sends a first electromagnetic signal with a first frequencyto a first side of a body and the second transceiver simultaneouslysends a second electromagnetic signal with a second frequency to asecond side of a body, wherein the first frequency and the secondfrequency are different frequencies, wherein the second side of the bodyis an opposite side of the body from the first side of the body, whereinthe first transceiver receives a first reflected electromagnetic signalreflected back in response to the first electromagnetic signal, andextracts out a first baseband complex signal from the first reflectedsignal; wherein the second transceiver receives a second reflectedelectromagnetic signal reflected back in response to the secondelectromagnetic signal, and extracts out a second baseband complexsignal from the second reflected signal; an apparatus configured tocombine the first baseband complex signal with the second basebandcomplex signal to cancel out a Doppler frequency drift therebetween toyield a periodic Doppler phase effect, wherein the apparatus comprises aprocessor and/or hardware.
 10. The system of claim 9, furthercomprising: a processor configured to extract at least one ofrespiration rate and heart rate from the periodic Doppler phase effect.11. The system of claim 10, further comprising: a display for displayingthe at least one of respiration rate and heart rate from the periodicDoppler phase effect.
 12. The system of claim 9, wherein the firstfrequency, corresponding to a first wavelength, λ₁, and the secondfrequency, corresponding to a second wavelength, λ₂, are close to eachother such that (λ₁≈λ₂≈λ) and combining the first baseband complexsignal with the second baseband complex signal comprises combining thefirst baseband complex signal with the second baseband complex signalvia complex signal demodulation expressed by $\begin{matrix}{{S_{f}(t)} = {\exp\left\{ {j\left\lbrack {\frac{4\pi\;{x_{h\; 1}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 1}(t)}}{\lambda} + \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{1}} \right\rbrack} \right\}}} \\{{S_{b}(t)} = {\exp\left\{ {j\left\lbrack {\frac{4\pi\;{x_{h\; 2}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 2}(t)}}{\lambda} - \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{2}} \right\rbrack} \right\}}}\end{matrix}$ where x_(h1)(t) and x_(r1)(t) are heartbeat-induced andrespiration-induced physiological movements on the first side of thebody, x_(h2)(t) and x_(r2)(t) are heartbeat-induced andrespiration-induced physiological movements on the second side of thebody, φ₁ and φ₂ are residual phases of the first transceiver and thesecond transceiver, and y(t) is a body movement, wherein by multiplyingS_(f)(t) and S_(b)(t), the y(t) term in the baseband output outputS_(fb)(t)=S_(f)(t)·S_(b)(t) will be cancelled out, while terms ofphysiological movement x_(h1)(t), x_(h2)(t), x_(r1)(t) and x_(h2)(t) areenhanced as expressed by${S_{f\; b}(t)} = {\exp{\left\{ {j\left\lbrack {\frac{4{\pi\left\lbrack {{x_{h\; 1}(t)} + {x_{h\; 2}(t)}} \right\rbrack}}{\lambda} + \frac{4{\pi\left\lbrack {{x_{r\; 1}(t)} + {x_{r\; 2}(t)}} \right\rbrack}}{\lambda} + \phi_{1} + \phi_{2}} \right\rbrack} \right\}.}}$13. The system of claim 12, wherein a DC offset in at least one of thefirst baseband and the second baseband is accurately calibrated.
 14. Thesystem of claim 9, wherein the first frequency corresponding to a firstwavelength, λ₁, and the second frequency, corresponding to a secondwavelength, λ₂, are close to each other such that (λ₁≈λ₂≈λ) and thecombining the first baseband complex signal with the second basebandcomplex signal comprises combining the first baseband complex signalwith the second baseband complex signal via arctangent demodulationexpressed by $\begin{matrix}{{\psi_{f}(t)} = {\frac{4\pi\;{x_{h\; 1}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 1}(t)}}{\lambda} + \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{1}}} \\{{\psi_{b}(t)} = {\frac{4\pi\;{x_{h\; 2}(t)}}{\lambda} + \frac{4\pi\;{x_{r\; 2}(t)}}{\lambda} - \frac{4\;\pi\;{y(t)}}{\lambda} + \phi_{2}}}\end{matrix}$ where x_(h1)(t) and x_(r1)(t) are heartbeat-induced andrespiration-induced physiological movements on the first side of thebody, x_(h1)(t) and x_(r1)(t) are heartbeat-induced andrespiration-induced physiological movements on the second side of thebody, φ₁ and φ₂ are residual phases of the first transceiver and thesecond transceiver, and y(t) is a body movement, wherein by addingψ_(f)(t) and ψ_(b)(t), the y(t) term in the baseband output outputψ_(fb)(t)=ψ_(f)(t)+ψ_(b)(t) will be cancelled out, while terms ofphysiological movement x_(h1)(t), x_(h2)(t), x_(r1)(t) and x_(h2)(t) areenhanced as expressed by${\psi_{f\; b}(t)} = {\frac{4{\pi\left\lbrack {{x_{h\; 1}(t)} + {x_{h\; 2}(t)}} \right\rbrack}}{\lambda} + \frac{4{\pi\left\lbrack {{x_{r\; 1}(t)} + {x_{r\; 2}(t)}} \right\rbrack}}{\lambda} + \phi_{1} + {\phi_{2}.}}$15. The system of claim 14, wherein a DC offset in the first basebandand the second baseband is accurately calibrated.
 16. The system ofclaim 9, wherein the first transceiver and the second transceiver areselected from the group consisting of: a 5.8 GHz quadrature radartransceiver; and a 24 GHz quadrature radar transceiver.